References

• Arobi, R., Rijon, R. H., Haque, N., Safkat, T. I. M., & Safoan, S. (2022). Sentiment analysis on E-commerce based product reviews using machine learning algorithms (Doctoral dissertation, Brac University). google scholar
• Anjaria, M., & Guddeti, R. M. R. (2014). Influence factor based opinion mining of Twitter data using supervised learning. In 2014 sixth international conference on communication systems and networks (COMSNETS) (pp. 1-8). IEEE. google scholar
• Bayhaqy, A., Sfenrianto, S., Nainggolan, K., & Kaburuan, E. R. (2018, October). Sentiment analysis about E-commerce from tweets using decision tree, K-nearest neighbor, and naîve baYes. İn 2018 International conference on orange Technologies (ICOT) (pp. 1-6). IEEE. google scholar
• Chaubey, G., Gavhane, P. R., Bisen, D., & Arjaria, S. K. (2023). Customer purchasing behavior prediction using machine learning classifi-cation techniques. Journal of Ambient Intelligence and Humanized Computing, 14(12), 16133-16157. google scholar
• Chen, N. (2022). Research on E-Commerce DataBase Marketing Based on Machine Learning ALgorithm. Computational Intelligence and Neuroscience, 2022(1), 7973446. google scholar
• Demircan, M., Seller, A., Abut, F., & AkaY, M. F. (2021). Developing Turkish sentiment analYsis models using machine learning and e-commerce data. International Journal of Cognitive Computing in Engineering, 2, 202-207. google scholar
• Deniz, E., ErbaY, H., & Coşar, M. (2022). Multi-label classification of e-commerce customer reviews via machine learning. Axioms, 11(9), 436. google scholar
• Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. google scholar
• Dharrao, D., Deokate, S., Bongale, A. M., & Urolagin, S. (2023, March). E-commerce product review classification based on supervised machine learning techniques. In 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 1934-1939). IEEE. google scholar
• George, A. S., & Baskar, T. (2024). Leveraging Big Data and Sentiment Analysis for Actionable Insights: A Review of Data Mining Approaches for Social Media. Partners Universal International Innovation Journal, 2(4), 39-59. google scholar
• Grandi, U., Loreggia, A., Rossi, F., & Saraswat, V. (2015). A borda count for collective sentiment analysis. Annals of Mathematics and Artificial Intelligence, 77(3-4), 281-302. https://doi.org/10.1007/s10472-015-9488-0 google scholar
• Gound, R. S., Tikone, P. V., Suryawanshi, S. S., & Nagpal, D. (2018). Twitter Data Sentiment Analysis and Visualization. International Journal of Computer Applications, 975, 8887. google scholar
• Guven, Z. A. (2021a). The effect of bert, electra and albert language models on sentiment analysis for turkish product reviews. In 2021 6th international conference on computer science and engineering (UBMK) (pp. 629-632). IEEE. google scholar
• Guven, Z. A. (2021b). Comparison of BERT models and machine learning methods for sentiment analysis on Turkish tweets. In 2021 6th International Conference on Computer Science and Engineering (UBMK) (pp. 98-101). IEEE. google scholar
• Haque, T. U., Saber, N. N., & Shah, F. M. (2018, May). Sentiment analysis on large scale Amazon product reviews. In 2018 IEEE international conference on innovative research and development (ICIRD) (pp. 1-6). IEEE. google scholar
• He, H., Zhou, G., & Zhao, S. (2022). Exploring e-commerce product experience based on fusion sentiment analysis method. IEEE Access, 10, 110248-110260. google scholar
• Huang, H., Asemi, A., & Mustafa, M. B. (2023). Sentiment analysis in e-commerce platforms: A review of current techniques and future directions. IEEE Access. google scholar
• Jabbar, J., Urooj, I., JunSheng, W., & Azeem, N. (2019). Real-time sentiment analysis on E-commerce application. In 2019 IEEE 16th international conference on networking, sensing and control (ICNSC) (pp. 391-396). IEEE. google scholar
• Koksal, D., Alacan, M. M., Olgun, E., & Sakar, C. O. (2022). Natural Language Processing-Based Product Category Classification Model for E-Commerce. In 2022 30th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE. google scholar
• Köksal, A., & Özgür, A. (2021). Twitter dataset and evaluation of transformers for Turkish sentiment analysis. In 2021 29th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE. google scholar
• Kotan, M. (2022). Duygu Analizi ve Dijital Dönüşüm Üzerine Etkileri. In Book: Dijital Etkileşimler ve Sektörel Yansımaları II. Gazi Kitapevi. google scholar
• Kubrusly, J., Neves, A. L., & Marques, T. L. (2022). A statistical analysis of textual e-commerce reviews using tree-based methods. Open Journal of Statistics, 12(3), 357-372. google scholar
• Kumar, K. S., Desai, J., & Majumdar, J. (2016, December). Opinion mining and sentiment analysis on online customer review. In 2016 IEEE international conference on computational intelligence and computing research (ICCIC) (pp. 1-4). IEEE. google scholar
• Marong, M., Batcha, N. K., & Mafas, R. (2020). Sentiment Analysis in E-Commerce: A Review on The Techniques and Algorithms. Journal of Applied Technology and Innovation, 4(1), 6. google scholar
• Polat, H., & Ağca, Y. (2022). Tripadvisor Kullanıcılarının Türkçe Ve İngilizce Yorumları Kapsamında Duygu Analizi Yöntemlerinin Karşılaştırmalı Analizi. Abant sosyal bilimler dergisi, 22(2), 901-916. google scholar
• Poyraz, Y. Z., Tugcu, M., & Amasyali, M. F. (2022). Improving BERT Pre-training with Hard Negative Pairs. In 2022 Innovations in Intelligent Systems and Applications Conference (ASYU) (pp. 1-6). IEEE. google scholar
• Rumelli, M., Akkuş, D., Kart, Ö., & Isik, Z. (2019, October). Sentiment analysis in Turkish text with machine learning algorithms. In 2019 Innovations in intelligent systems and applications conference (ASYU) (pp. 1-5). IEEE. google scholar
• Qin, Z., Dong, K., & Xie, B. (2024). What affects customers’ online shopping behavior, research that applied machine learning to Amazon product reviews. Applied and Computational Engineering, 76, 48-64. google scholar
• Saleem, H., Muhammad, K. B., Nizamani, A. H., Saleem, S., & Aslam, A. M. (2019). Data science and machine learning approach to improve E-commerce sales performance on social web. International Journal of Computer Science and Network Security (IJCSNS), 19. google scholar
• Savci, P., & Das, B. (2023). Prediction of the customers’ interests using sentiment analysis in e-commerce data for comparison of Arabic, English, and Turkish languages. Journal of King Saud University-Computer and Information Sciences, 35(3), 227-237. google scholar
• Sayeed, M. S., Mohan, V., & Muthu, K. S. (2023). BERT: A Review of Applications in Sentiment Analysis. HighTech and Innovation Journal, 4(2), 453-462. google scholar
• Schweter, S. (2020). Berturk—Bert models for turkish. Zenodo. https://doi.org/10.5281/zenodo.3770924 google scholar
• Shanto, S. S., Ahmed, Z., & Jony, A. I. (2023). Mining User Opinions: A Balanced Bangla Sentiment Analysis Dataset for E-Commerce. Malaysian Journal of Science and Advanced Technology, 272-279. google scholar
• Shehu, H. A., & Tokat, S. (2020). A hybrid approach for the sentiment analysis of Turkish Twitter data. In Artificial Intelligence and Applied Mathematics in Engineering Problems: Proceedings of the International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2019) (pp. 182-190). Springer International Publishing. google scholar
• Şimşek, İ., Kökçam, A. H., Demir, H. I., Erden, C. (2023). Sentiment Analysis of Twitter Data of Hepsiburada E-commerce Site Customers with Natural Language Processing. In International Symposium on Intelligent Manufacturing and Service Systems (pp. 567-578). Singapore: Springer Nature Singapore. google scholar
• Tang, D., Qin, B., Liu, T., & Yang, Y. (2015, June). User modeling with neural network for review rating prediction. In Twenty-fourth international joint conference on artificial intelligence. google scholar
• Taşcı, M. E., Rasheed, J., & Özkul, T. (2024, January). Sentiment Analysis on Reviews of Amazon Products Using Different Machine Learning Algorithms. In International Conference on Forthcoming Networks and Sustainability in the AIoT Era (pp. 318-327). Cham: Springer Nature Switzerland. google scholar
• Stephenie, Warsito, B., & Prahutama, A. (2020). Sentiment analysis on tokopedia product online reviews using random forest method. In E3S Web of Conferences (Vol. 202, p. 16006). EDP Sciences. google scholar
• X Help Center. (2024). About different types of posts. Retrieved from https://help.twitter.com/en/using-x/types-of-posts google scholar
• Xu, F., Pan, Z., & Xia, R. (2020). E-commerce product review sentiment classification based on a naıve BaYes continuous learning framework. Information Processing & Management, 57(5), 102221. google scholar
• Yang, L., Li, Y., Wang, J., & Sherratt, R. S. (2020). Sentiment analYsis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning. IEEE Access, 8, 23522-23530. google scholar
• Yildırım, S. (2024). Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks. arXiv preprint arXiv:2401.17396. google scholar
• Yıldırım, S., & Asgari-Chenaghlu, M. (2021). Mastering Transformers: Build state-of-the-art models from scratch with advanced natural language processing techniques. Packt Publishing Ltd. google scholar
• Yıldız, H. (2020). The Role Of Machine Learning Algorithms In Determining Product Sales In E-Commerce: A Case StudY For TurkeY. Current and Historical Debates in Social Sciences: Field Studies and Analysis, 175. google scholar
• Zada, A. J. J., & AlbaYrak, A. (2023). DuYgu Analizi ve Topluluk Öğrenmesi Yaklaşımları ile Kullanıcı Yorumlarının Analizi. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 11(4), 1725-1732. google scholar

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